Serveur d'exploration sur le lymphœdème

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Tumor-induced lymph node alterations detected by MRI lymphography using gadolinium nanoparticles

Identifieur interne : 007A66 ( Ncbi/Merge ); précédent : 007A65; suivant : 007A67

Tumor-induced lymph node alterations detected by MRI lymphography using gadolinium nanoparticles

Auteurs : S. C. Partridge [États-Unis] ; B. F. Kurland [États-Unis] ; C.-L. Liu [États-Unis] ; R. J. Y. Ho [États-Unis] ; A. Ruddell [États-Unis]

Source :

RBID : PMC:4620490

Abstract

Contrast-enhanced MRI lymphography shows potential to identify alterations in lymph drainage through lymph nodes (LNs) in cancer and other diseases. MRI studies have typically used low molecular weight gadolinium contrast agents, however larger gadolinium-loaded nanoparticles possess characteristics that could improve the specificity and sensitivity of lymphography. The performance of three gadolinium contrast agents with different sizes and properties was compared by 3T MRI after subcutaneous injection. Mice bearing B16-F10 melanoma footpad tumors were imaged to assess tumor-induced alterations in lymph drainage through tumor-draining popliteal and inguinal LNs versus contralateral uninvolved drainage. Gadolinium lipid nanoparticles were able to identify tumor-induced alterations in contrast agent drainage into the popliteal LN, while lower molecular weight or albumin-binding gadolinium agents were less effective. All of the contrast agents distributed in foci around the cortex and medulla of tumor-draining popliteal LNs, while they were restricted to the cortex of non-draining LNs. Surprisingly, second-tier tumor-draining inguinal LNs exhibited reduced uptake, indicating that tumors can also divert LN drainage. These characteristics of tumor-induced lymph drainage could be useful for diagnosis of LN pathology in cancer and other diseases. The preferential uptake of nanoparticle contrasts into tumor-draining LNs could also allow selective targeting of therapies to tumor-draining LNs.


Url:
DOI: 10.1038/srep15641
PubMed: 26497382
PubMed Central: 4620490

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PMC:4620490

Le document en format XML

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<p>Contrast-enhanced MRI lymphography shows potential to identify alterations in lymph drainage through lymph nodes (LNs) in cancer and other diseases. MRI studies have typically used low molecular weight gadolinium contrast agents, however larger gadolinium-loaded nanoparticles possess characteristics that could improve the specificity and sensitivity of lymphography. The performance of three gadolinium contrast agents with different sizes and properties was compared by 3T MRI after subcutaneous injection. Mice bearing B16-F10 melanoma footpad tumors were imaged to assess tumor-induced alterations in lymph drainage through tumor-draining popliteal and inguinal LNs versus contralateral uninvolved drainage. Gadolinium lipid nanoparticles were able to identify tumor-induced alterations in contrast agent drainage into the popliteal LN, while lower molecular weight or albumin-binding gadolinium agents were less effective. All of the contrast agents distributed in foci around the cortex and medulla of tumor-draining popliteal LNs, while they were restricted to the cortex of non-draining LNs. Surprisingly, second-tier tumor-draining inguinal LNs exhibited reduced uptake, indicating that tumors can also divert LN drainage. These characteristics of tumor-induced lymph drainage could be useful for diagnosis of LN pathology in cancer and other diseases. The preferential uptake of nanoparticle contrasts into tumor-draining LNs could also allow selective targeting of therapies to tumor-draining LNs.</p>
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</author>
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</author>
<author>
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</author>
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</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Lambregts, D M" uniqKey="Lambregts D">D. M. Lambregts</name>
</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Kwon, S" uniqKey="Kwon S">S. Kwon</name>
</author>
<author>
<name sortKey="Sevick Muraca, E M" uniqKey="Sevick Muraca E">E. M. Sevick-Muraca</name>
</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
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</author>
<author>
<name sortKey="Croft, A" uniqKey="Croft A">A. Croft</name>
</author>
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</author>
</analytic>
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<biblStruct>
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</analytic>
</biblStruct>
</listBibl>
</div1>
</back>
</TEI>
<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Sci Rep</journal-id>
<journal-id journal-id-type="iso-abbrev">Sci Rep</journal-id>
<journal-title-group>
<journal-title>Scientific Reports</journal-title>
</journal-title-group>
<issn pub-type="epub">2045-2322</issn>
<publisher>
<publisher-name>Nature Publishing Group</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">26497382</article-id>
<article-id pub-id-type="pmc">4620490</article-id>
<article-id pub-id-type="pii">srep15641</article-id>
<article-id pub-id-type="doi">10.1038/srep15641</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Tumor-induced lymph node alterations detected by MRI lymphography using gadolinium nanoparticles</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Partridge</surname>
<given-names>S. C.</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="aff" rid="a2">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Kurland</surname>
<given-names>B. F.</given-names>
</name>
<xref ref-type="aff" rid="a3">3</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>C.-L.</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="aff" rid="a2">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ho</surname>
<given-names>R. J. Y.</given-names>
</name>
<xref ref-type="aff" rid="a4">4</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ruddell</surname>
<given-names>A.</given-names>
</name>
<xref ref-type="corresp" rid="c1">a</xref>
<xref ref-type="aff" rid="a5">5</xref>
<xref ref-type="aff" rid="a6">6</xref>
</contrib>
<aff id="a1">
<label>1</label>
<institution>Seattle Cancer Care Alliance</institution>
, Seattle WA
<country>USA</country>
</aff>
<aff id="a2">
<label>2</label>
<institution>Department of Radiology, University of Washington</institution>
, Seattle WA
<country>USA</country>
</aff>
<aff id="a3">
<label>3</label>
<institution>Department of Biostatistics, University of Pittsburgh</institution>
, Pittsburgh, PA</aff>
<aff id="a4">
<label>4</label>
<institution>Department of Pharmaceutics, University of Washington</institution>
, Seattle WA
<country>USA</country>
</aff>
<aff id="a5">
<label>5</label>
<institution>Department of Comparative Medicine</institution>
, Seattle WA
<country>USA</country>
</aff>
<aff id="a6">
<label>6</label>
<institution>Fred Hutchinson Cancer Research Center</institution>
, Seattle WA
<country>USA</country>
</aff>
</contrib-group>
<author-notes>
<corresp id="c1">
<label>a</label>
<email>ruddella@uw.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>26</day>
<month>10</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection">
<year>2015</year>
</pub-date>
<volume>5</volume>
<elocation-id>15641</elocation-id>
<history>
<date date-type="received">
<day>22</day>
<month>05</month>
<year>2015</year>
</date>
<date date-type="accepted">
<day>23</day>
<month>09</month>
<year>2015</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright © 2015, Macmillan Publishers Limited</copyright-statement>
<copyright-year>2015</copyright-year>
<copyright-holder>Macmillan Publishers Limited</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/">
<pmc-comment>author-paid</pmc-comment>
<license-p>This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
</license-p>
</license>
</permissions>
<abstract>
<p>Contrast-enhanced MRI lymphography shows potential to identify alterations in lymph drainage through lymph nodes (LNs) in cancer and other diseases. MRI studies have typically used low molecular weight gadolinium contrast agents, however larger gadolinium-loaded nanoparticles possess characteristics that could improve the specificity and sensitivity of lymphography. The performance of three gadolinium contrast agents with different sizes and properties was compared by 3T MRI after subcutaneous injection. Mice bearing B16-F10 melanoma footpad tumors were imaged to assess tumor-induced alterations in lymph drainage through tumor-draining popliteal and inguinal LNs versus contralateral uninvolved drainage. Gadolinium lipid nanoparticles were able to identify tumor-induced alterations in contrast agent drainage into the popliteal LN, while lower molecular weight or albumin-binding gadolinium agents were less effective. All of the contrast agents distributed in foci around the cortex and medulla of tumor-draining popliteal LNs, while they were restricted to the cortex of non-draining LNs. Surprisingly, second-tier tumor-draining inguinal LNs exhibited reduced uptake, indicating that tumors can also divert LN drainage. These characteristics of tumor-induced lymph drainage could be useful for diagnosis of LN pathology in cancer and other diseases. The preferential uptake of nanoparticle contrasts into tumor-draining LNs could also allow selective targeting of therapies to tumor-draining LNs.</p>
</abstract>
</article-meta>
</front>
<floats-group>
<fig id="f1">
<label>Figure 1</label>
<caption>
<title>Gd-LNP contrast media detects increased lymph drainage through the tumor-draining popliteal lymph node.</title>
<p>(
<bold>a</bold>
) Full field-of-view oblique MIP image, illustrating the
<italic>in vivo</italic>
locations of the left and right popliteal (LPN and RPN, respectively) and inguinal LNs (LIN and RIN) that were analyzed in the study. The MIP was generated from 5 min post- GD-LNP images, in the same animal shown in part c. (
<bold>b</bold>
) Representative single slice images of LPN and RPN (arrows) from pre-contrast (0 min) and from 5 and 15 minute post-contrast scans after Gd-DTPA injection demonstrate modest LN enhancement after contrast injection. The orientation of the cortex and medulla is indicated. Arrowhead indicates the dark artifacts arising after contrast agent injection. (
<bold>c</bold>
) Single slice images of popliteal LNs after Gd-LNP injection show higher uptake into the LPN at 5 and 15 min. (
<bold>d</bold>
) Single slice images of popliteal LNs after Gd-FVT injection show uptake into the LPN and RPN at 5 min after contrast agent injection. Scale bars are indicated.</p>
</caption>
<graphic xlink:href="srep15641-f1"></graphic>
</fig>
<fig id="f2">
<label>Figure 2</label>
<caption>
<title>Slice-by-slice illustration of Gd-LNP uptake distribution through lymph nodes.</title>
<p>Serial images sampled through the entire left and right popliteal LNs before and 5 min after subcutaneous injection of Gd-LNP. Comparison of pre- and post-contrast images show Gd-LNP enhancement in the medulla and cortex of the LPN, while contrast uptake is more restricted to the cortex of the RPN of all slices. Arrows indicate the popliteal LNs. Note that the RPN images have been flipped vertically to facilitate comparison of the LPN and RPN anatomy.</p>
</caption>
<graphic xlink:href="srep15641-f2"></graphic>
</fig>
<fig id="f3">
<label>Figure 3</label>
<caption>
<title>Hot-spot analysis of contrast agent uptake into the popliteal lymph nodes.</title>
<p>For each LN, histogram analysis was used to determine the 90
<sup>th</sup>
percentile pre-contrast threshold, and to select only post-contrast voxels above the same threshold value for integrated density calculations and analysis. Shown are representative MR images before (0 min) and after (5 min) Gd-LNP injection (left panels), associated LN signal intensity histograms (center panels), and thresholded maps (right panels). (
<bold>a</bold>
) In the LPN, the 90
<sup>th</sup>
percentile pre-contrast value of 139 was determined from the histogram, which corresponded to the 61
<sup>st</sup>
percentile of 5 min post-contrast pixel values. Pixels with values above 139 and included in this analysis are shown in red on threshold maps. (
<bold>b</bold>
) The process was repeated for the RPN, where the 90
<sup>th</sup>
percentile pre-contrast value was 141, which corresponded to the 77
<sup>th</sup>
percentile of 5 min post-contrast pixel values.</p>
</caption>
<graphic xlink:href="srep15641-f3"></graphic>
</fig>
<fig id="f4">
<label>Figure 4</label>
<caption>
<title>Quantitation of contrast agent uptake in tumor-draining and uninvolved popliteal lymph nodes.</title>
<p>Integrated density values over each popliteal LN were calculated using the 90
<sup>th</sup>
percentile threshold method. Change from pre-contrast integrated density is summarized for groups of 6 mice receiving each contrast agent, with standard error bars displayed at each timepoint. (
<bold>a</bold>
) The integrated density of Gd-DTPA contrast uptake into ROIs increases within 5 min after injection in the LPN (
<sup>#</sup>
p = 0.01) and RPN (
<sup>#</sup>
p = 0.006), as estimated in a linear mixed effects regression model. (
<bold>b</bold>
) The integrated density of Gd-LNP contrast uptake increases within 5 min after injection for the tumor-draining LPN (
<sup>#</sup>
p = 0.009), but not for the RPN (p = 0.24). Moreover, the contrast uptake into the LPN is significantly higher than into the RPN at both 5 and 15 min after injection (*p < 0.01). (
<bold>c</bold>
) Gd-FVT uptake into the LPN and RPN 5 min after injection is statistically significant (
<sup>#</sup>
p = 0.02 for both), although the difference in contrast uptake between the LPN and RPN is not significantly different at 5 min (p = 0.14) or 15 min (p = 0.52).</p>
</caption>
<graphic xlink:href="srep15641-f4"></graphic>
</fig>
<fig id="f5">
<label>Figure 5</label>
<caption>
<title>Contrast agent uptake in the inguinal lymph nodes.</title>
<p>(
<bold>a</bold>
) The uptake of Gd-DTPA into the LIN and RIN (arrows) is shown in single slice examples. Gd-DTPA uptake is minimally detected in the RIN, and not in the LIN. (
<bold>b</bold>
) Gd-LNP is taken up into the RIN but not into the LIN. (
<bold>c</bold>
) Gd-FVT contrast agent accumulates in the RIN and LIN, at 5 and at 15 min after injection. Scale bars are indicated.</p>
</caption>
<graphic xlink:href="srep15641-f5"></graphic>
</fig>
<fig id="f6">
<label>Figure 6</label>
<caption>
<title>Quantitation of contrast agent uptake in inguinal lymph nodes.</title>
<p>Integrated density values in each inguinal LN were calculated using the 90
<sup>th</sup>
percentile threshold method. Change from pre-contrast integrated density is summarized for groups of 6 mice receiving each contrast agent, with standard error bars displayed at each timepoint. (
<bold>a</bold>
) Gd-DPTA uptake into the RIN appears to be greater than into the LIN at 5 min after injection (p = 0.07) and at 15 min after injection (*p = 0.009). (
<bold>b</bold>
) Gd-LNP also accumulates in the RIN more than the LIN at 5 min after injection (*p = 0.01) and at 15 min after injection (*p = 0.03). (
<bold>c</bold>
) Similar trends were seen for Gd-FVT, with contrast uptake greater into the RIN than into the LIN at 5 min after injection (p = 0.11) and at 15 min (*p = 0.04). In most cases the integrated density of contrast uptake was not found to increase within 5 min after injection (p > 0.05 in linear mixed effects model), with the exception of the RIN for Gd-DTPA (average 213,000 increase,
<sup>#</sup>
p = 0.002) and RIN for Gd-FVT (average 913,000 increase,
<sup>#</sup>
p = 0.02).</p>
</caption>
<graphic xlink:href="srep15641-f6"></graphic>
</fig>
<fig id="f7">
<label>Figure 7</label>
<caption>
<title>Gd-FVT labels the central lymphatic drainage.</title>
<p>Pre-contrast (t = 0 min) MIP of slices in the spine region, and at 15 min after Gd-FVT injection identify the femoral lymphatic vessels (solid lines) draining toward the central iliac LNs. Scale bars are indicated.</p>
</caption>
<graphic xlink:href="srep15641-f7"></graphic>
</fig>
<fig id="f8">
<label>Figure 8</label>
<caption>
<title>Tumors alter lymph drainage.</title>
<p>The lymph drainage pattern in the tumor-bearing left leg is altered relative to the uninvolved right leg. Drainage from the tumor to the LPN increases, continuing on to the left iliac LN (LIL), while the drainage to the LIN is reduced. In the uninvolved right leg, lymph drains normally to the RPN and on to the right iliac LN (RIL) and RIN.</p>
</caption>
<graphic xlink:href="srep15641-f8"></graphic>
</fig>
<table-wrap position="float" id="t1">
<label>Table 1</label>
<caption>
<title>Detection of contrast agents in lymph nodes.</title>
</caption>
<table frame="hsides" rules="groups" border="1">
<colgroup>
<col align="left"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
</colgroup>
<thead valign="bottom">
<tr>
<th rowspan="2" align="left" valign="bottom" charoff="50"> </th>
<th colspan="3" align="center" valign="top" charoff="50">Normal Lymph Node
<hr></hr>
</th>
<th colspan="3" align="center" valign="top" charoff="50">Tumor-draining Lymph Node
<hr></hr>
</th>
</tr>
<tr>
<th align="center" valign="top" charoff="50">Gd-DTPA</th>
<th align="center" valign="top" charoff="50">Gd-LNP</th>
<th align="center" valign="top" charoff="50">Gd-FVT</th>
<th align="center" valign="top" charoff="50">Gd-DTPA</th>
<th align="center" valign="top" charoff="50">Gd-LNP</th>
<th align="center" valign="top" charoff="50">Gd-FVT</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left" valign="top" charoff="50">Popliteal lymph node</td>
<td align="center" valign="top" charoff="50">+</td>
<td align="center" valign="top" charoff="50">+/−</td>
<td align="center" valign="top" charoff="50">++</td>
<td align="center" valign="top" charoff="50">+</td>
<td align="center" valign="top" charoff="50">+++</td>
<td align="center" valign="top" charoff="50">++</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50">Inguinal lymph node</td>
<td align="center" valign="top" charoff="50">+</td>
<td align="center" valign="top" charoff="50">+</td>
<td align="center" valign="top" charoff="50">++</td>
<td align="center" valign="top" charoff="50"></td>
<td align="center" valign="top" charoff="50"></td>
<td align="center" valign="top" charoff="50">+/−</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="t1-fn1">
<p>Symbols.
<sup>+++</sup>
strong labeling,
<sup>++</sup>
moderate labeling,
<sup>+</sup>
weak labeling,
<sup>+/−</sup>
very weak labeling,
<sup></sup>
no labeling.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</floats-group>
</pmc>
<affiliations>
<list>
<country>
<li>États-Unis</li>
</country>
<region>
<li>Pennsylvanie</li>
</region>
</list>
<tree>
<country name="États-Unis">
<noRegion>
<name sortKey="Partridge, S C" sort="Partridge, S C" uniqKey="Partridge S" first="S. C." last="Partridge">S. C. Partridge</name>
</noRegion>
<name sortKey="Ho, R J Y" sort="Ho, R J Y" uniqKey="Ho R" first="R. J. Y." last="Ho">R. J. Y. Ho</name>
<name sortKey="Kurland, B F" sort="Kurland, B F" uniqKey="Kurland B" first="B. F." last="Kurland">B. F. Kurland</name>
<name sortKey="Liu, C L" sort="Liu, C L" uniqKey="Liu C" first="C.-L." last="Liu">C.-L. Liu</name>
<name sortKey="Liu, C L" sort="Liu, C L" uniqKey="Liu C" first="C.-L." last="Liu">C.-L. Liu</name>
<name sortKey="Partridge, S C" sort="Partridge, S C" uniqKey="Partridge S" first="S. C." last="Partridge">S. C. Partridge</name>
<name sortKey="Ruddell, A" sort="Ruddell, A" uniqKey="Ruddell A" first="A." last="Ruddell">A. Ruddell</name>
<name sortKey="Ruddell, A" sort="Ruddell, A" uniqKey="Ruddell A" first="A." last="Ruddell">A. Ruddell</name>
</country>
</tree>
</affiliations>
</record>

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